Advertisement

The Journal of Supercomputing

, Volume 74, Issue 8, pp 4037–4059 | Cite as

QUICK: QoS-guaranteed efficient cloudlet placement in wireless metropolitan area networks

  • Long Chen
  • Jigang Wu
  • Gangqiang Zhou
  • Longjie Ma
Article

Abstract

This article defines the QoS-guaranteed efficient cloudlet deployment problem in wireless metropolitan area network, which aims to minimize the average access delay of mobile users, i.e., the average delay when service requests are successfully sent and being served by cloudlets. Meanwhile, we try to optimize total deployment cost represented by the total number of deployed cloudlets. For the first target, both un-designated capacity and constrained capacity cases are studied, and we have designed efficient heuristic and clustering algorithms, respectively. We show our algorithms are more efficient than the existing algorithm. For the second target, we formulate an integer linear programming to minimize the number of used cloudlets with given average access delay requirement. A clustering algorithm is devised to guarantee the scalability. For a special case of the deployment cost optimization problem where all cloudlets’ computing capabilities have been given, i.e., designated capacity, an efficient heuristic algorithm is further proposed to minimize the number of cloudlets. We finally evaluate the performance of proposed algorithms through extensive experimental simulations. Simulation results demonstrate the proposed algorithms are more than \(46\%\) efficient than existing algorithms on the average cloudlet access delay. Compared with existing algorithms, our proposed clustering and heuristic algorithms can reduce the number of deployed cloudlets by about \(50\%\) averagely, owing to the calculation processes of shortest paths between APs and the sorting processes of user access delays.

Keywords

Cloudlet Access delay Cloud computing Heuristic Clustering 

References

  1. 1.
    Barioni MCN, Razente HL, Traina AJ, Traina C (2008) Accelerating k-medoid-based algorithms through metric access methods. J Syst Softw 81(3):343–355CrossRefGoogle Scholar
  2. 2.
    Bonomi F, Milito R, Zhu J, Addepalli S (2012) Fog computing and its role in the internet of things. In: Edition of the Mcc workshop on mobile cloud computing, pp 13–16. ACMGoogle Scholar
  3. 3.
    Bourdena A, Mavromoustakis C, Mastorakis G, Rodrigues J, Dobre C (2015) Using socio-spatial context in mobile cloud offload process for energy conservation in wireless devices. IEEE Trans Cloud ComputGoogle Scholar
  4. 4.
    Cai W, Leung VC, Hu L (2014) A cloudlet-assisted multiplayer cloud gaming system. Mob Netw Appl 19(2):144–152CrossRefGoogle Scholar
  5. 5.
    Charikar M, Guha S, Tardos Shmoys DB (1999) A constant-factor approximation algorithm for the k -median problem (extended abstract). In: ACM Symposium on Theory of Computing, pp 1–10Google Scholar
  6. 6.
    Chen L, Wu J, Dai HN, Huang X (2018) Brains: Joint bandwidth-relay allocation in multi-homing cooperative d2d networks. IEEE Trans Veh Technol 1–12.  https://doi.org/10.1109/TVT.2018.2799970
  7. 7.
    Chen L, Wu J, Zhang XX, Zhou G (2018) Tarco: Two-stage auction for d2d relay aided computation resource allocation in hetnet. IEEE Trans Serv Comput 1–14.  https://doi.org/10.1109/TSC.2018.2792024
  8. 8.
    Cormen TH, Leiserson CE, Rivest RL, Stein C (2001) Introduction to algorithms. second editionGoogle Scholar
  9. 9.
    Fan Q, Ansari N (2017) Cost aware cloudlet placement for big data processing at the edge. In: IEEE International Conference on Communications, pp 1–6Google Scholar
  10. 10.
    Fazio P, De Rango F, Tropea M (2017) Prediction and qos enhancement in new generation cellular networks with mobile hosts: a survey on different protocols and conventional/unconventional approaches. IEEE Commun Surv Tutor 19(3):1822–1841CrossRefGoogle Scholar
  11. 11.
    Gu L, Zeng D, Barnawi A, Guo S, Stojmenovic I (2015) Optimal task placement with qos constraints in geo-distributed data centers using dvfs. IEEE Trans Comput 64(7):2049–2059MathSciNetCrossRefGoogle Scholar
  12. 12.
    Gt-itm (2017). http://www.cc.gatech.edu/projects/gtitm/. [Online; accessed 10-May-2017]
  13. 13.
    Hoang DT, Niyato D, Wang P (2012) Optimal admission control policy for mobile cloud computing hotspot with cloudlet. In: Wireless Communications and Networking Conference (WCNC), pp 3145–3149. IEEE, Shanghai, ChinaGoogle Scholar
  14. 14.
    Huang H, Guo S (2017) Service provisioning update scheme for mobile application users in a cloudlet network. In: IEEE International Conference on Communications (ICC), pp 1–6Google Scholar
  15. 15.
    Ieee standards for local and metropolitan area networks: Overview and architecture (ansi) (1990)Google Scholar
  16. 16.
    Jia M, Cao J, Liang W (2017) Optimal cloudlet placement and user to cloudlet allocation in wireless metropolitan area networks. IEEE Trans Cloud Comput 5(4):725–737CrossRefGoogle Scholar
  17. 17.
    Jin A, Song W, Wang P, Niyato D, Ju P (2016) Auction mechanisms toward efficient resource sharing for cloudlets in mobile cloud computing. IEEE Trans Serv Comput 9(6):895–909CrossRefGoogle Scholar
  18. 18.
    Kosta S, Aucinas A, Hui P, Mortier R, Zhang X (2012) Thinkair: Dynamic resource allocation and parallel execution in the cloud for mobile code offloading. In: Proceedings of Infocom, pp 945–953. IEEEGoogle Scholar
  19. 19.
  20. 20.
    Ma L, Wu J, Chen L (2017) Dota: Delay bounded optimal cloudlet deployment and user association in wmans. In: 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), pp 196–203. IEEE, Madrid, SpainGoogle Scholar
  21. 21.
    Pang Z, Sun L, Wang Z, Tian E, Yang S (2015) A survey of cloudlet based mobile computing. In: International Conference on Cloud Computing and Big Data, pp 268–275. IEEE, Shanghai, ChinaGoogle Scholar
  22. 22.
    Ren S, Schaar MVD (2014) Dynamic scheduling and pricing in wireless cloud computing. IEEE Trans Mob Comput 13(10):2283–2292CrossRefGoogle Scholar
  23. 23.
    Rimal BP, Van DP, Maier M (2017) Cloudlet enhanced fiber-wireless access networks for mobile-edge computing. IEEE Trans Wirel Commun 16(6):3601–3618CrossRefGoogle Scholar
  24. 24.
    Satyanarayanan M, Bahl P, Caceres R, Davies N (2009) The case for vm-based cloudlets in mobile computing. IEEE Pervasive Comput 8(4):14–23Google Scholar
  25. 25.
    Shaukat U, Ahmed E, Anwar Z, Xia F (2016) Cloudlet deployment in local wireless networks: motivation, architectures, applications, and open challenges. J Netw Comput Appl 62(3):18–40CrossRefGoogle Scholar
  26. 26.
    Vaquero LM, Rodero-Merino L (2014) Finding your way in the fog: towards a comprehensive definition of fog computing. ACM SIGCOMM Comput Commun Rev 44(5):27–32CrossRefGoogle Scholar
  27. 27.
    Verbelen T, Simoens P, De Turck F, Dhoedt B (2012) Cloudlets: bringing the cloud to the mobile user. In: Proceedings of the Third ACM workshop on Mobile Cloud Computing and Services, pp 29–36. ACM, Low Wood Bay, UKGoogle Scholar
  28. 28.
    Xu Z, Liang W, Xu W, Jia M (2015) Capacitated cloudlet placements in wireless metropolitan area networks. In: Local Computer Networks, pp 570–578Google Scholar
  29. 29.
    Xu Z, Liang W, Xu W, Jia M, Guo S (2016) Efficient algorithms for capacitated cloudlet placements. IEEE Trans Parallel Distrib Syst 27(10):2866–2880CrossRefGoogle Scholar
  30. 30.
    Zhang Y, Liu H, Jiao L, Fu X (2012) To offload or not to offload: an efficient code partition algorithm for mobile cloud computing. In: the 1st International Conference on Cloud Networking (CLOUDNET), pp 80–86. IEEE, Paris, FranceGoogle Scholar
  31. 31.
    Zhao L, Sun W, Shi Y, Liu J (2018) Optimal placement of cloudlets for access delay minimization in sdn-based internet of things networks. IEEE Internet Things J 5(2):1334–1344Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyGuangdong University of Technology (GDUT)GuangzhouChina

Personalised recommendations